{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: A Simple Agent Based Model In Python\n",
    "\n",
    "##### Authors: Bill Thompson (biltho@mpi.nl) and Limor Raviv (limor.raviv@mpi.nl)\n",
    "Please let us know if you have any comments, suggestions or questions regarding this notebook.\n",
    "\n",
    "---------------\n",
    "\n",
    "## Summary\n",
    "In this second tutorial, we will build our first simple network of agents using the commands in the first notebook.\n",
    "We will create a network of multiple simple agents who interact in random dyads, and simulate changes in the community over multiple interactions. \n",
    "In each interaction, one agent (the producer) produces one of two possible vowel variants according to their existing representations. Then, the second agent (the listener) can either stick to their existing variant, or adapt to the producer by changing their vowel accordingly. The listener's behavior is based on biased \"personalities\": agents can be flexible (adapt to their partner) or stubborn (not adapt).\n",
    "We repeat this process multiple times and see what happens to the variants in the population at large. We will try to answer questions like: Has one of the variants spread to the entire community? Does this depend on the community's size and inital structure? How many stubborn people must be present to prevent (or faciliate?) convergence? etc.\n",
    "\n",
    "-------------- \n",
    "\n",
    "\n",
    "### 1. Setting the network\n",
    "First, let's create lists containing the possible parameters for our agents. We will create a seperate list for the possible vowel representations and possible personalties our agents can have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the parameters\n",
    "\n",
    "vowels = ['a', 'i']\n",
    "\n",
    "personalities = ['F', 'S'] # F= Flexible, S=Stubborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's write a simple function to create agents. An agent is just represented as a list with two values (a vowel and a personality)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'F']\n"
     ]
    }
   ],
   "source": [
    "def make_agent(vowel, personality):\n",
    "    return [vowel, personality]\n",
    "\n",
    "# For examaple, we can create a flexible agent with the vowel 'i' using our function\n",
    "\n",
    "agent_one = make_agent(vowels[1], personalities[0])\n",
    "print(agent_one)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can write functions that make populations of N agents (again, in the form of a list):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['i', 'F'], ['i', 'F'], ['i', 'F'], ['i', 'F'], ['i', 'F']]\n"
     ]
    }
   ],
   "source": [
    "# Create a function that generates a population of N identical agents with given parameters\n",
    "\n",
    "def make_population_identical(N):\n",
    "    \n",
    "    population = []\n",
    "    \n",
    "    for i in range(N):\n",
    "        \n",
    "        agent = make_agent(vowels[1], personalities[0])\n",
    "        \n",
    "        population.append(agent)\n",
    "\n",
    "    return population\n",
    "\n",
    "\n",
    "# Call the function to make a population of 5 identical agents\n",
    "\n",
    "population_test = make_population_identical(5)\n",
    "print(population_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function that generates of population of N agents with randomly selected parameters from each list\n",
    "# using \"random.choice()\"\n",
    "\n",
    "import random\n",
    "\n",
    "def make_population_random(N):\n",
    "    \n",
    "    population = []\n",
    "    \n",
    "    for i in range(N):\n",
    "        \n",
    "        v = random.choice(vowels)\n",
    "        \n",
    "        p = random.choice(personalities)\n",
    "        \n",
    "        agent = make_agent(v, p)\n",
    "        \n",
    "        population.append(agent)\n",
    "\n",
    "    return population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run the box of code below multiple times to make sure you are really getting random populations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['a', 'S'], ['i', 'F'], ['i', 'F'], ['i', 'S'], ['i', 'S']]\n"
     ]
    }
   ],
   "source": [
    "# Call the function to make a population of 5 random agents\n",
    "population = make_population_random(5)\n",
    "print(population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['i', 'F'], ['i', 'F'], ['a', 'F'], ['a', 'F'], ['i', 'S'], ['i', 'S'], ['a', 'F'], ['i', 'S']]\n"
     ]
    }
   ],
   "source": [
    "# You can achieve the same goal using \"random.int()\" and using the index of the lists of possible parameters\n",
    "\n",
    "def make_population(N):\n",
    "    \n",
    "    population = []\n",
    "    \n",
    "    for i in range(N):\n",
    "        \n",
    "        v = random.randint(0,1)\n",
    "        \n",
    "        p = random.randint(0,1)\n",
    "        \n",
    "        agent = make_agent(vowels[v], personalities[p])\n",
    "        \n",
    "        population.append(agent)\n",
    "\n",
    "    return population\n",
    "\n",
    "# Call the funtion and make a population of 8 random agents\n",
    "# You can play with the numbers to make bigger or smaller populations\n",
    "pop = make_population(8)\n",
    "print(pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function that calculates the proportion of agents with the variant 'a' in the population\n",
    "\n",
    "def count(population):\n",
    "    t = 0. # must be a float!     \n",
    "    for agent in population:\n",
    "        if agent[0] == 'a':\n",
    "            t += 1            # The syntax =+ Adds 1 to t (or: t = t + 1)\n",
    "    return t / len(population)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a given population, we can now check how many agents are using each possible vowel variant. This is important, because later we'll also want to see how the proportion of each variant changes over the course of multiple interactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this, we'll write a function that calculates the proportion of a given vowel in a population:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proportion of [a] in the population is 0.7\n"
     ]
    }
   ],
   "source": [
    "# Call the funtion on a population of 20 random agents\n",
    "# You can run this box multiple times to see the proportion in different populations of different sizes\n",
    "\n",
    "prop_a = count(make_population(20))\n",
    "\n",
    "print('The proportion of [a] in the population is', prop_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Interaction time!\n",
    "We have a population, and now we want the agents to interact with each other. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So first, we need to make a function that randomly selects two agents from the population:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The population is [['a', 'S'], ['i', 'F'], ['i', 'F'], ['i', 'S'], ['i', 'S']]\n",
      "This is the chosen pair ['a', 'S'] ['i', 'F']\n",
      "The listener is ['a', 'S']\n",
      "The producer is ['i', 'F']\n"
     ]
    }
   ],
   "source": [
    "from numpy.random import choice\n",
    "\n",
    "def choose_pair(population):\n",
    "    i = random.randint(0, len(population) - 1) # phyton counts from 0, so pop(8) is an error\n",
    "    j = random.randint(0, len(population) - 1)\n",
    "    \n",
    "    while i == j:\n",
    "        j = random.randint(0, len(population) - 1) # make sure the same agent is not selected twice\n",
    "        \n",
    "    return population[i], population[j]\n",
    "\n",
    "\n",
    "# And we'll test it to see that really does what we want\n",
    "# You can run this box of code multiple times to make sure you are really getting random pairs\n",
    "\n",
    "pop = make_population(8)\n",
    "listener, producer = choose_pair(pop)\n",
    "\n",
    "print('The population is', population)\n",
    "print('This is the chosen pair', listener, producer)\n",
    "print('The listener is', listener)\n",
    "print('The producer is', producer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's write a function that makes this pair \"interact\"!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the producer and listener have the same vowel representation, nothing changes. If the agents have different vowels, then the listener's action depends on their prior personality: if they are stubborn, they will not change their vowel; but if they are flexible, they will update their vowel according to the producer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So if the listener is flexible and has a different variant than the producer, we want to update the listener's vowel based on the producer's vowel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do this, we'll need to use a function called \"deepcopy\" to make a copy of the producer rather than pointing to the producer itself, because otherwise Python will have these two agents linked togeher forever. This is of course unwanted, since we want to update the listener only once based on a single interaction. Therefore, we'll use function called \"deepcopy\", which basically does what we want except for not linking the actual agents together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "\n",
    "def interact_test(listener,producer): \n",
    "    \n",
    "    if listener[0] == producer[0]:\n",
    "        return listener # if the listener and producer have the same vowel, no change\n",
    "    else:\n",
    "        if listener[1]=='S':\n",
    "            return listener # if the listener is stubborn, no change\n",
    "        else:\n",
    "            listener[0]=deepcopy(producer[0])\n",
    "            return listener"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check the output of the loop by running the code line below multiple times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The listener is ['i', 'F']\n",
      "The producer is ['i', 'S']\n",
      "After ineracting, the listener is ['i', 'F']\n"
     ]
    }
   ],
   "source": [
    "randomlistener, randomproducer = choose_pair(make_population(8))\n",
    "\n",
    "print('The listener is', randomlistener)\n",
    "print('The producer is', randomproducer)\n",
    "\n",
    "updated_listener = interact_test(randomlistener, randomproducer)\n",
    "\n",
    "print('After ineracting, the listener is',updated_listener)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we have a tested function that updates agents after interaction. Since we don't actually need the function to return the listener as output, we can change it to have no output, only to update the agents if needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function that only updates agents using \"pass\" (which means do nothing in Python)\n",
    "\n",
    "def interact(listener,producer): \n",
    "    \n",
    "    if listener[0] == producer[0]:\n",
    "        pass   # do nothing\n",
    "    else:\n",
    "        if listener[1]=='S':\n",
    "            pass\n",
    "        else:\n",
    "            listener[0]=deepcopy(producer[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, we're almost ready to run some simulations! So far, we've created a a few basic functions: \n",
    "1. Make Agent - creating an agent that has a vowel and a personality\n",
    "2. Make Population - creating a population of agents using the function in (1)\n",
    "3. Count - counting the proportion of agents with the same vowel in the population created by (2)\n",
    "4. Choose Pair - choosing two agents out of the population created in (2)\n",
    "5. Interact - implementing the interaxction between the two agents chosen in (4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Simulation time!\n",
    "The next step is to create a function that loops over the entire population based on a given number of desired interactions and check how many agents are using each possible vowel variant over time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function that simulates a community of size N interacting randomly for K times       \n",
    "\n",
    "def simulate(n, k):\n",
    "    \n",
    "    population = make_population(n)\n",
    "    \n",
    "    # print(\"Initial Population:\", population)\n",
    "    \n",
    "    proportion = [] # make an empty list to keep track of the porportions after every interaction\n",
    "    \n",
    "    for i in range(k):\n",
    "        \n",
    "        pair = choose_pair(population) # choose a pair from the population\n",
    "        \n",
    "        interact(pair[0],pair[1])  # make the chosen pair interact\n",
    "        \n",
    "        proportion.append(count(population)) # track the proportion of the vowels in the population during intrtaction\n",
    "    \n",
    "    return population, proportion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that our function works by plotting the change in proportions of vowels over time. Again, you can run this multiple times to see what happens in different communities, and also change the numbers as you wish. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Population: [['a', 'F'], ['a', 'F'], ['a', 'S'], ['a', 'S'], ['i', 'F'], ['a', 'S'], ['i', 'F'], ['a', 'S'], ['i', 'F'], ['a', 'S'], ['i', 'S'], ['a', 'S'], ['a', 'F'], ['i', 'F'], ['a', 'F'], ['i', 'S'], ['a', 'F'], ['a', 'F'], ['a', 'S'], ['i', 'S']]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda2c319be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulate 500 interctions between 20 agents \n",
    "new_population, proportion = simulate(20, 500)\n",
    "print(\"Final Population:\", new_population)\n",
    "\n",
    "# Make a plot of the changes in proportion of 'a' over interactions \n",
    "\n",
    "%matplotlib inline \n",
    "#put plot in the notebook\n",
    "import matplotlib.pyplot as plt # importing a plotting library\n",
    "plt.plot(proportion)\n",
    "\n",
    "# and add some details to the plot\n",
    "plt.title('Changes in the proportion of [a] over time')\n",
    "plt.ylabel('Proportion [a] users')\n",
    "plt.xlabel('Time [No. of interactions]')\n",
    "plt.ylim(0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep in mind that bigger communities and more interactions will have more reliable results, but also will take more time to compute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   \n",
      "Changes in the proportion of [a] over time\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda04056400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulate 5000 interctions between 200 agents \n",
    "new_population, proportion = simulate(200, 5000)\n",
    "#print(\"Final Population:\", new_population)\n",
    "\n",
    "# Make a plot of the changes in proportion of 'a' over interactions \n",
    "print('   ')\n",
    "print('Changes in the proportion of [a] over time')\n",
    "plt.plot(proportion)\n",
    "plt.ylim(0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wonderful, this works! But as you can see, there is a lot of variance in the results of different simulated populations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a real picture of what's going on, we'll need to run multiple simulations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's write a new function that makes a bunch of simulations at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function that runs s simulations of a community of size N interacting randomly for K times    \n",
    "\n",
    "def batch_simulate(n,k,s):\n",
    "    batch_proportions=[]\n",
    "    for i in range(s):\n",
    "        new_population, proportion = simulate(n, k)\n",
    "        batch_proportions.append(proportion)\n",
    "    return batch_proportions\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check if the function works by plotting the results of this batch simulation. Again, you can run this multiple times and manipulate the size of the community (n), the number of interactions (k) or the number of simulations (s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda0408a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make 20 simulations of 5000 interctions between 200 agents \n",
    "results = batch_simulate(200,5000,20)\n",
    "\n",
    "plt.ylim(0,1)\n",
    "\n",
    "for i in results:\n",
    "    plt.plot(i)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Let's try to make a prediction and test it!\n",
    "You made it! You've just run your first simple sound change simulation!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps you noticed that the trend is towards not converging: even after 5000 interactions, the proportion of one variant is still around 0.5, meaning the both vowels are prevelant in the popuation and there hasn't been a change towards one vowel. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is because we start out with random populations in which some stubborn agents never change. We predict that if there are at least 2 stubborn agents with *different* vowels, the flexible agents in the community will keep adapting to these *different* stubborn agents and never reaching stability and convergence. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we check ourselves: under which conditions can the community converge? How many stubborn people can there be in a population and still have convergence? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test this, we will modify our functions to create biased populations, where we control the number of stubborn agents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify the function to make populations of N agents with a given number of stubborn agents (st)\n",
    "\n",
    "\n",
    "def make_population_biased(N,st):\n",
    "    \n",
    "    population = []\n",
    "    \n",
    "    for i in range(st):\n",
    "        \n",
    "        v = random.randint(0,1)\n",
    "        \n",
    "        agent = make_agent(vowels[v], personalities[1])\n",
    "        \n",
    "        population.append(agent)\n",
    "    \n",
    "    for i in range(N-st):\n",
    "        \n",
    "        v = random.randint(0,1)\n",
    "        \n",
    "        agent = make_agent(vowels[v], personalities[0])\n",
    "        \n",
    "        population.append(agent)\n",
    "\n",
    "    return population\n",
    "\n",
    "\n",
    "# Modify the function so that it calls our biased population \n",
    "\n",
    "def simulate_biased(n, k, st):  #st=no. of stubborn\n",
    "    \n",
    "    population = make_population_biased(n,st)\n",
    "    \n",
    "    # print(\"Initial Population:\", population)\n",
    "    \n",
    "    proportion = []\n",
    "    \n",
    "    for i in range(k):\n",
    "        \n",
    "        pair = choose_pair(population)\n",
    "        \n",
    "        interact(pair[0],pair[1])\n",
    "        \n",
    "        proportion.append(count(population))\n",
    "    \n",
    "    return population, proportion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what happens in  communities with no stubborn agents. Run this multiple times to see if any population reaches convergence (getting close to values of 1 or 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changes in the proportion of [a] over time\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd9fcd46ba8>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9fcb60550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run a simulation in a community with no stubborn agents\n",
    "\n",
    "new_population, proportion = simulate_biased(200, 5000, 0)\n",
    "print('Changes in the proportion of [a] over time')\n",
    "plt.ylim(0,1)\n",
    "plt.plot(proportion)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about when there's one stubborn agent? Or two?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changes in the proportion of [a] over time\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd9fcd6d390>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9fcb58fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run a simulation in a community with 1 stubborn agents\n",
    "\n",
    "new_population, proportion = simulate_biased(200, 5000, 1)\n",
    "print('Changes in the proportion of [a] over time')\n",
    "plt.ylim(0,1)\n",
    "plt.plot(proportion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changes in the proportion of [a] over time\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd9fcd109b0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9fcd7c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run a simulation in a community with 2 stubborn agents\n",
    "\n",
    "new_population, proportion = simulate_biased(200, 5000, 2)\n",
    "print('Changes in the proportion of [a] over time')\n",
    "plt.ylim(0,1)\n",
    "plt.plot(proportion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, there is too much variation. So let's run these simulations multiple times, unsing a wide range of proportions of stubborn people."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll run S simulations for each possible proportion of stubborn people.\n",
    "Here, we'll check what happens when there are 0, 1, 2, 25%, 50% and 100% stubborn agents in the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify the function so it runs S simulations of each biased population\n",
    "\n",
    "def batch_simulate_biased(n,k,s): #n-pop size, k=no. of interactions, s=no. of simulations for each bias\n",
    "    \n",
    "    all_results=[]\n",
    "    \n",
    "    possible_sts = [0, 1, 2, int(n / 4.), int(n / 2.), n]\n",
    "    \n",
    "    for possible_st in possible_sts:\n",
    "        \n",
    "        print(possible_st)\n",
    "    \n",
    "        current_results = []  # print the progress of the simulations \n",
    "    \n",
    "        for i in range(s):\n",
    "\n",
    "            new_population, proportion = simulate_biased(n, k, possible_st)\n",
    "            current_results.append(proportion)\n",
    "            \n",
    "        all_results.append(current_results)\n",
    "    \n",
    "    return all_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can check how the proportion of stubborn agents affects convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "50\n",
      "100\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "# Run 20 simulations of each stubborness proportions in a community of 200 agents \n",
    "results = batch_simulate_biased(200,5000,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Proportion Individuals Using [a]')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9fcd3b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results of the simulations \n",
    "\n",
    "colors = ['green', 'blue', 'red', 'purple', 'orange', 'pink']\n",
    "for j, st in enumerate(results):\n",
    "    for simulation in st:\n",
    "        plt.plot(simulation, color = colors[j], alpha = .5)\n",
    "\n",
    "plt.ylim(0,1)\n",
    "plt.xlabel('No. of Interactions')\n",
    "plt.ylabel('Proportion Individuals Using [a]')       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we see is that there are no clear patterns, except for when the population has 0-2 stubborn agents (in red, blue and green): in these cases, the population usually converges on one vowel. In all other cases, it's basically a mess.\n",
    "If you want to practice you plotting skills, try adding a legend to this plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great job! "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
